• Journal of Korea Water Resources Association
• /
• v.31 no.1
• /
• pp.45-57
• /
• 1998

The purpose of this paper is to develop a neural network model in order to forecast flood inflow into the reservoir that has the nature of uncertainty and nonlinearity. The model has the features of multi-layered structure and parallel multi-connections. To develop the model. backpropagation learning algorithm was used with the Momentum and Levenberg-Marquardt techniques. The former technique uses gradient descent method and the later uses gradient descent and Gauss-Newton method respectively to solve the problems of local minima and for the speed of convergency. Used data for learning are continuous fixed real values of input as well as output to emulate the real physical aspects. after learning process. a reservoir inflows forecasting model at flood period was constructed. The data for learning were used to calibrate the developed model and the results were very satisfactory. applicability of the model to the Chungju Mlultipurpose Reservoir proved the availability of the developed model.

• Progress in Medical Physics
• /
• v.12 no.2
• /
• pp.141-146
• /
• 2001

It is well known that assurance of the radiation therapy needs for an accuracy of $\pm$ 5 % in the delivery of an absorbed dose to target volume. Therefore, the dose evaluation of brachytherapy source and/or linear accelerate beam must be a stability with accuracy. In an advanced country, they recommended to use the radioactive check source for reference air ionization chamber for a stable response of radiation field chamber. In this experiments, the radioactive source Sr-90 and PR-05 air ionization chamber were used for standard source and reference ion chamber. The response of reference ion chamber showed as an 1.000$\pm$ 0.010 uncertainty for 10 years long and the evaliuation f dose discrepancy of clinical field ion chamber showed as 0.997 $\pm$0.011 in a $^{60}$ Co brachytherapy soruce. In our experiments, we can assuarance the long halflife standard source is reliable to preserve the calibration factor of reference chamber in stability.

• Nuclear Engineering and Technology
• /
• v.41 no.7
• /
• pp.945-952
• /
• 2009

The Three Mile Island Unit 2 (TMI-2) accident provides unique full scale data, thus providing opportunities to check the capability of codes to model overall plant behavior and to perform a spectrum of sensitivity and uncertainty calculations. As part of the TMI-2 analysis benchmark exercise sponsored by the Organization for Economic Cooperation and Development Nuclear Energy Agency (OECD NEA), several member countries are continuing to improve their system analysis codes using the TMI-2 data. The Republic of Korea joined this benchmark exercise in November 2005. Seoul National University has analyzed the TMI-2 accident as well as the currently proposed alternative scenario along with a sensitivity study using the Modular Accident Analysis Program Version 4.03 (MAAP4.03) code in collaboration with the Korea Hydro and Nuclear Power Company. Two input files are required to simulate the TMI-2 accident with MAAP4: the parameter file and an input deck. The user inputs various parameters, such as volumes or masses, for each component. The parameter file contains the information on TMI-2 relevant to the plant geometry, system performance, controls, and initial conditions used to perform these benchmark calculations. The input deck defines the operator actions and boundary conditions during the course of the accident. The TMI-2 accident analysis provided good estimates of the accident output data compared with the OECD TMI-2 standard reference. The alternative scenario has proposed the initial event as a loss of main feed water and a small break on the hot leg. Analysis is in progress along with a sensitivity study concerning the break size and elevation.

• Journal of Korea Technology Innovation Society
• /
• v.14 no.1
• /
• pp.85-108
• /
• 2011

Under Ford system, corporations sought to maximize the economies of scale by raising the production efficiency. It aims to lower the production cost by increasing the quantity of output. But in the era of market flux and uncertainty, however, such strategies can no longer be sustained. Replacing the structures of Ford system, Toyota was able to accelerate the pace of process innovation and product innovation. Related to this innovation is JIT, new model development, modularization. The firm's reliance on flexible production technology provides opportunities to expand her production basis to foreign countries successfully. The main objective of this paper is to explore the contribution of process innovation to profit-capital ratio. The model is estimated using a time-series data of 18 years from 1990 to 2007 of auto production industry in korea. An Implication of this estimation shows that process innovation explains a significant portion of profit-capital ratio.

• Journal of Korea Technology Innovation Society
• /
• v.21 no.2
• /
• pp.818-837
• /
• 2018

Technological innovation has inherent difficulties, largely due to the uncertainties of technology. Thus, the forecasting methodology to reduce the risk of uncertainty in the innovation process has been presented both in quantitative and qualitative fields. On the other hand, big data and artificial intelligence have attracted great interest recently, and deep learning, which is one of the algorithms of AlphaGo, is showing excellent performance. In this study, deep learning methodology was applied to the prediction of innovation performance. To make the prediction model, we used KIS 2016 data. The input factors were importance of information source and innovation objectives and the output factor was innovation performance index, which was calculated for this study. As a result of the analysis, it can be confirmed that the accuracy of prediction is improved compared with the previous studies. As learning progressed, the degree of freedom of prediction also improved.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2010.05a
• /
• pp.268-272
• /
• 2010

The research presented here represents a collaborative effort with the SFWMD on developing scenarios for future climate for the SFWMD area. The project focuses on developing methodology for simulating precipitation representing both natural quasi-oscillatory modes of variability in these climate variables and also the secular trends projected by the IPCC scenarios that are publicly available. This study specifically provides the results for precipitation modeling. The starting point for the modeling was the work of Tebaldi et al that is considered one of the benchmarks for bias correction and model combination in this context. This model was extended in the framework of a Hierarchical Bayesian Model (HBM) to formally and simultaneously consider biases between the models and observations over the historical period and trends in the observations and models out to the end of the 21st century in line with the different ensemble model simulations from the IPCC scenarios. The low frequency variability is modeled using the previously developed Wavelet Autoregressive Model (WARM), with a correction to preserve the variance associated with the full series from the HBM projections. The assumption here is that there is no useful information in the IPCC models as to the change in the low frequency variability of the regional, seasonal precipitation. This assumption is based on a preliminary analysis of these models historical and future output. Thus, preserving the low frequency structure from the historical series into the future emerges as a pragmatic goal. We find that there are significant biases between the observations and the base case scenarios for precipitation. The biases vary across models, and are shrunk using posterior maximum likelihood to allow some models to depart from the central tendency while allowing others to cluster and reduce biases by averaging. The projected changes in the future precipitation are small compared to the bias between model base run and observations and also relative to the inter-annual and decadal variability in the precipitation.

• Journal of the Economic Geographical Society of Korea
• /
• v.12 no.4
• /
• pp.454-476
• /
• 2009

The purpose of this paper is to examine relation between trust building level and linkages's spatial characteristics on the logistics & storage industry of Busan. As a result, First, long-term & repeated interaction, information sharing & reciprocity, and interdependence & asset specificity have an important effect upon the micro trust which implies the highest trust. Proximity and uncertainty impact on the meso trust, the trust of middle level. Culture, norm, and formal institution of firms affect the macro trust, the lowest level of trust. Second, the micro, the meso, and the macro trusts mainly form in the local scale where spatial proximity is great. The higher the trust building levels become, the more spatial dimensions by linkage expand to national and international dimension, respectively. Third, these results appear more clearly in the output linkage than input linkage, in the service areas-many firm, in the horizontal linkage than vertical linkage, in the advanced evolution phases of firm connection, and in the supply chain management than outsourcing.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.1
• /
• pp.168-180
• /
• 2018

In order to achieve the high quality output voltage of single-phase voltage source inverters, in this paper an Adaptive Complementary Sliding Mode Control (ACSMC) is proposed. Firstly, the dynamics model of the single-phase inverter with lumped uncertainty including parameter variations and external disturbances is derived. Then, the conventional Sliding Mode Control (SMC) and Complementary Sliding Mode Control (CSMC) are introduced separately. However, when system parameters vary or external disturbance occurs, the controlling performance such as tracking error, response speed et al. always could not satisfy the requirements based on the SMC and CSMC methods. Consequently, an ACSMC is developed. The ACSMC is composed of a CSMC term, a compensating control term and a filter parameters estimator. The compensating control term is applied to compensate for the system uncertainties, the filter parameters estimator is used for on-line LC parameter estimation by the proposed adaptive law. The adaptive law is derived using the Lyapunov theorem to guarantee the closed-loop stability. In order to decrease the control system cost, an inductor current estimator is developed. Finally, the effectiveness of the proposed controller is validated through Matlab/Simulink and experiments on a prototype single-phase inverter test bed with a TMS320LF28335 DSP. The simulation and experimental results show that compared to the conventional SMC and CSMC, the proposed ACSMC control strategy achieves more excellent performance such as fast transient response, small steady-state error, and low total harmonic distortion no matter under load step change, nonlinear load with inductor parameter variation or external disturbance.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.17 no.5
• /
• pp.401-408
• /
• 2012

A simple on-line elimination strategy of the dead time and inverter nonlinearity using the current slope information is presented for a PWM inverter-fed IPMSM (Interior Permanent Magnet Synchronous Motor) drive. In a PWM inverter-fed IPMSM drive, a dead time is inserted to prevent a breakdown of switching device. This distorts the inverter output voltage, resulting in a current distortion and torque ripple. In addition to the dead time, inverter nonlinearity exists in switching devices of the PWM inverter, which is generally dependent on operating conditions such as the temperature, DC link voltage, and current. The proposed scheme is based on the fact that the d-axis current ripple is mainly caused by the dead time and inverter nonlinearity. To eliminate such an influence, the current slope information is determined. The obtained current slope information is processed by the PI controller to estimate the disturbance caused by the dead time and inverter nonlinearity. The overall system is implemented using DSP TMS320F28335 and the validity of the proposed algorithm is verified through the simulation and experiments. Without requiring any additional hardware, the proposed scheme can effectively eliminate the dead time and inverter nonlinearity even in the presence of the parameter uncertainty.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.27 no.4
• /
• pp.339-344
• /
• 2007

Safety of large scale cable in bridge is very important because it may cause the unwanted catastrophic failure. Although the proof load were considered at the design stage, its soundness must be monitored continuously because the cable may be broken out without warning by the variable external load. The cable tension of in-use structures has been mainly measured by the resonance method and its use has been limited because of relatively large measurement uncertainty. Recently a new magnetic method was developed and its reliability is known to be good for evaluating the cable tension. In this study a system which can deliver the calibrated load to the cable was developed and the measurement reliability of developed magnetic sensor according to the change of external load was analyzed quantitatively. The effect of magnetization frequency, bias magnetic field, and temperature on the sensor output was also evaluated.